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Hagura N.,University College London | Hagura N.,ATR Brain Information Communication Research Laboratory Group | Kanai R.,University College London | Orgs G.,University College London | Haggard P.,University College London
Proceedings of the Royal Society B: Biological Sciences

Professional ball game players report the feeling of the ball 'slowing-down' before hitting it. Because effective motor preparation is critical in achieving such expert motor performance, these anecdotal comments imply that the subjective passage of time may be influenced by preparation for action. Previous reports of temporal illusions associated with action generally emphasize compensation for suppressed sensory signals that accompany motor commands. Here, we show that the time is perceived slowed-down during preparation of a ballistic reaching movement before action, involving enhancement of sensory processing. Preparing for a reaching movement increased perceived duration of a visual stimulus. This effect was tightly linked to action preparation, because the amount of temporal dilation increased with the information about the upcoming movement. Furthermore, we showed a reduction of perceived frequency for flickering stimuli and an enhanced detection of rapidly presented letters during action preparation, suggesting increased temporal resolution of visual perception during action preparation. We propose that the temporal dilation during action preparation reflects the function of the brain to maximize the capacity of sensory information-acquisition prior to execution of a ballistic movement. This strategy might facilitate changing or inhibiting the planned action in response to last-minute changes in the external environment. © 2012 The Royal Society. Source

Samek W.,TU Berlin | Samek W.,Bernstein Center for Computational Neuroscience | Blythe D.,TU Berlin | Blythe D.,Bernstein Center for Computational Neuroscience | And 3 more authors.
Advances in Neural Information Processing Systems

The efficiency of Brain-Computer Interfaces (BCI) largely depends upon a reliable extraction of informative features from the high-dimensional EEG signal. A crucial step in this protocol is the computation of spatial filters. The Common Spatial Patterns (CSP) algorithm computes filters that maximize the difference in band power between two conditions, thus it is tailored to extract the relevant information in motor imagery experiments. However, CSP is highly sensitive to artifacts in the EEG data, i.e. few outliers may alter the estimate drastically and decrease classification performance. Inspired by concepts from the field of information geometry we propose a novel approach for robustifying CSP. More precisely, we formulate CSP as a divergence maximization problem and utilize the property of a particular type of divergence, namely beta divergence, for robustifying the estimation of spatial filters in the presence of artifacts in the data. We demonstrate the usefulness of our method on toy data and on EEG recordings from 80 subjects. Source

Kawato M.,ATR Brain Information Communication Research Laboratory Group
2013 International Winter Workshop on Brain-Computer Interface, BCI 2013

Japanese MEXT started SRPBS (strategic research for promotion of brain sciences) in 2008. Field A was on BMI and I am the leader of this. I will describe achievement within this large group funding. Within ATR, we have developed next generation noninvasive decoding method as well as decoded neurofeedback method. © 2013 IEEE. Source

Kawato M.,ATR Brain Information Communication Research Laboratory Group
2014 International Winter Workshop on Brain-Computer Interface, BCI 2014

One of the most important assumptions in neuroscience and brain science is that neural activity in the brain is the cause of our mind including consciousness. Most studies of human learning/memory/cognition have concentrated on examining correlations between behavioral and neural activity changes rather than establishing cause-and-effect relationships. Even for animal studies, the most frequently used technique is examining temporal correlation between neural activities and some hypothetical computational variables proposed by experimenters. The lack of experimental tools examining cause and effect relationships in the systems neuroscience severely constrains its progress and applicability to practical problems. To fill this gap between major concepts and current technology, by applying a novel online-feedback method utilizing decoded functional magnetic resonance imaging (fMRI) signals, we developed a new technique to manipulate neural codes [1], DecNef. © 2014 IEEE. Source

Kajimura S.,Kyoto University | Kochiyama T.,ATR Brain Information Communication Research Laboratory Group | Nakai R.,Kyoto University | Abe N.,Kyoto University | Nomura M.,Kyoto University
Psychiatry Research - Neuroimaging

Social anxiety disorder (SAD), which involves excessive anxiety and fear of negative evaluation, is accompanied by abnormalities in brain function. While social anxiety appears to be represented on a spectrum ranging from nonclinical behavior to clinical manifestation, neural alteration in nonclinical populations remains unclear. This study examined the relationship between psychological measures of social anxiety, mainly using the Fear of Negative Evaluation Scale (FNES), and brain function (functional connectivity, degree centrality, and regional betweenness centrality). Results showed that FNES scores and functional connectivity of the parahippocampal gyrus and orbitofrontal cortex and the betweenness centrality of the right parietal cortex were negatively correlated. These regions are altered in SAD patients, and each is associated with social cognition and emotional processing. The results supported the perspective that social anxiety occurs on a spectrum and indicated that the FNES is a useful means of detecting neural alterations that may relate to the social anxiety spectrum. In addition, the findings indicated that graph analysis was useful in investigating the neural underpinnings of SAD in addition to other psychiatric symptoms. © 2015 Elsevier Ireland Ltd. Source

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